Bayesian force fields from active learning for simulation of inter-dimensional transformation of stanene
Bayesian force fields from active learning for simulation of inter-dimensional transformation of stanene
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DOI：
10.1038/s41524-021-00510-y
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发表时间：
2021-03-19
影响因子：
9.7
通讯作者：
Kozinsky, Boris
中科院分区：
文献类型：
--
作者：
Xie, Yu;Vandermause, Jonathan;Kozinsky, Boris
我们提出了一种方法来显着加速高斯过程模型的原子间力场的基础上多体内核的映射力和不确定性的低维功能。这允许模型的自动主动学习，结合了接近量子的准确性，内置的不确定性和与经典分析模型相当的恒定评估成本，能够模拟数百万个原子。使用这种方法，我们进行大规模的分子动力学模拟的稳定性的锡烯单层。我们发现了一个不寻常的2D锡烯的相变机制，波纹导致成核的双层缺陷，致密化成一个无序的多层结构，然后在高温下形成大量的液体或成核和生长的3D bcc晶体在低温下。该方法为快速开发用于模拟复杂材料长时间大规模动态的快速准确的不确定性感知模型提供了可能性。
We present a way to dramatically accelerate Gaussian process models for interatomic force fields based on many-body kernels by mapping both forces and uncertainties onto functions of low-dimensional features. This allows for automated active learning of models combining near-quantum accuracy, built-in uncertainty, and constant cost of evaluation that is comparable to classical analytical models, capable of simulating millions of atoms. Using this approach, we perform large-scale molecular dynamics simulations of the stability of the stanene monolayer. We discover an unusual phase transformation mechanism of 2D stanene, where ripples lead to nucleation of bilayer defects, densification into a disordered multilayer structure, followed by formation of bulk liquid at high temperature or nucleation and growth of the 3D bcc crystal at low temperature. The presented method opens possibilities for rapid development of fast accurate uncertainty-aware models for simulating long-time large-scale dynamics of complex materials.